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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Bone age assessment based on deep learning architecture Alaa Jamal Jabbar; Ashwan A. Abdulmunem
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2078-2085

Abstract

The fast advancement of technology has prompted the creation of automated systems in a variety of sectors, including medicine. One application is an automated bone age evaluation from left-hand X-ray pictures, which assists radiologists and pediatricians in making decisions about the growth status of youngsters. However, one of the most difficult aspects of establishing an automated system is selecting the best approach for producing effective and dependable predictions, especially when working with large amounts of data. As part of this work, we investigate the use of the convolutional neural networks (CNNs) model to classify the age of the bone. The work’s dataset is based on the radiological society of North America (RSNA) dataset. To address this issue, we developed and tested deep learning architecture for autonomous bone assessment, we design a new deep convolution network (DCNN) model. The assessment measures that use in this work are accuracy, recall, precision, and F-score. The proposed model achieves 97% test accuracy for bone age classification.
Long term temperature stability of thermal cycler developed using low profile microprocessor cooler Setyawan Purnomo Sakti; Adin Okta Triqadafi; Aldi Dwi Putra; Triswantoro Putro; Dewi Anggraeni
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp278-287

Abstract

Developing a low-cost thermal cycler for a polymerase chain reaction (PCR) is becoming interested in the pandemic era caused by viruses. PCR is the standard gold for the diagnostic. However, in a low-income country, the availability of the device is limited. In this work, the development of a thermal cycler uses electronic modules available in the market. The central part is thermoelectric for heating and cooling, an embedded system to control, and a low-profile cooling fan. The system temperature control used a combination of feedforward, bang-bang, and proportional-integral-derivative (PID) control. The control parameter of the PID was successfully obtained by using Chien servo tuning. The feedforward and bang-bang control are used to optimize the cooling cycle and minimize the rise time. The system shows a well-suited temperature accuracy at the denaturation, annealing, and extension temperature with a temperature deviation of less than 0.5 °C. System performance is maintained even though the system has been running non-stop for 24 hours. The low-profile cooling fan, which is usually used for CPU cooling, shows good results in maintaining temperature stability.
An internet of things enabled framework to monitor the lifecycle of Cordyceps sinensis mushrooms Minakshi Memoria; Sanjeev Kumar Shah; Harishchander Anandaram; Anooja Ali; Kapil Joshi; Parag Verma; Rajesh Singh; Anita Gehlot; Shaik Vaseem Akram
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1142-1151

Abstract

Cordyceps sinensis is an edible mushroom found in high quantities in the regions of the Himalayas and widely considered in traditional systems of medicine. It is a non-toxic remedy mushroom and has a high measure of clinical medical benefits including cancer restraint, high blood pressure, diabetes, asthma, depression, fatigue, immune disorder, and many infections of the upper respiratory tract. The cultivation of this kind of mushroom is limited to the region of the Sikkim and to cultivate in the other regions of the country, they are need of investigation and prediction of cordyceps sinensis mushroom lifecycle. From the studies, it is concluded that the precision-based agriculture techniques are limitedly explored for the prediction and growth of Cordyceps sinensis mushrooms. In this study, an internet of things (IoT) inspired framework is proposed to predict the lifecycle of Cordyceps sinensis mushrooms and also provide alternate substrate to cultivate Cordyceps sinensis mushrooms in other parts of the country. As a part of lifecycle prediction, a framework is proposed in this study. According to the findings, an IoT sensor-based system with the ideal moisture level of the mushroom rack is required for the growth of Cordyceps sinensis mushrooms.
Self-admitted technical debt classification using natural language processing word embeddings Ahmed F. Sabbah; Abualsoud A. Hanani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2142-2155

Abstract

Recent studies show that it is possible to detect technical dept automatically from source code comments intentionally created by developers, a phenomenon known as self-admitted technical debt. This study proposes a system by which a comment or commit is classified as one of five dept types, namely, requirement, design, defect, test, and documentation. In addition to the traditional term frequency-inverse document frequency (TF-IDF), several word embeddings methods produced by different pre-trained language models were used for feature extraction, such as Word2Vec, GolVe, bidirectional encoder representations from transformers (BERT), and FastText. The generated features were used to train a set of classifiers including naive Bayes (NB), random forest (RF), support vector machines (SVM), and two configurations of convolutional neural network (CNN). Two datasets were used to train and test the proposed systems. Our collected dataset (A-dataset) includes a total of 1,513 comments and commits manually labeled. Additionally, a dataset, consisting of 4,071 labeled comments, used in previous studies (M-dataset) was also used in this study. The RF classifier achieved an accuracy of 0.822 with A-dataset and 0.820 with the M-dataset. CNN with A-dataset achieved an accuracy of 0.838 using BERT features. With M-dataset, the CNN achieves an accuracy of 0.809 and 0.812 with BERT and Word2Vec, respectively.
Forecasting movie rating using k-nearest neighbor based collaborative filtering Prakash Pandharinath Rokade; PVRD Prasad Rao; Aruna Kumari Devarakonda
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6506-6512

Abstract

Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.
Efficient systematic turbo polar decoding based on optimized scaling factor and early termination mechanism Ahmed A. Hamad; Mohammed Taih Gatte; Laith Ali Abdul-Rahaim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp629-637

Abstract

In this paper, an efficient early termination (ET) mechanism for systematic turbo-polar code (STPC) based on optimal estimation of scaling factor (SF) is proposed. The gradient of the regression line which best fits the distance between a priori and extrinsic information is used to estimate the SF. The multiplication of the extrinsic information by the proposed SF presents effectiveness in resolving the correlation issue between intrinsic and extrinsic reliability information traded between the two typical parallel concatenated soft-cancellation (SCAN) decoders. It is shown that the SF has improved the conventional STPC by about 0.3 dB with an interleaver length of 64 bits, and about 1 dB over the systematic polar code (SPC) at a bit error rate (BER) of . A new scheme is proposed as a stopping criterion, which is mainly based on the estimated value of SF at the second component decoder and the decoded frozen bits for each decoding iteration. It is shown that the proposed ET results in halving the average number of iterations (ANI) without adding considerable complexity. Moreover, the modified codes present comparable results in terms of BER to the codes that utilize fix number of iterations.
Maximum power point tracking controller using Lyapunov theorem of wind turbine under varying wind conditions Maamar Yahiaoui; Benameur Afif; Brahim Brahmi; Mohamed Horch; Mohamed Serraoui
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1281-1290

Abstract

Due to the instantaneous variation in wind speed, it is necessary to identify the optimal rotational speed that ensures maximum energy efficiency and system stability. We proposed a controller based on the Lyapunov theorem to extract the maximum power from wind speed and to ensure the overall stability of the controlled system under random operating conditions imposed by wind speed and parameter variations. The control of the Tip speed ratio is based on the Lyapunov theorem (TSR_LT), which is a controller based on Lyapunov's theory and the definition of a positive, energetic function, to ensure the stability of the system being controlled, the dynamics of this function must be negative. The viability of this work is demonstrated by MATLAB-based mathematical and simulation models and a comparison with the results obtained using proportional integral (PI) controller-based tip speed ratio control (TSR_PI controller). The simulation results demonstrate the controller's effectiveness.
Deep learning for cancer tumor classification using transfer learning and feature concatenation Abdallah Mohamed Hassan; Mohamed Bakry El-Mashade; Ashraf Aboshosha
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6736-6743

Abstract

Deep convolutional neural networks (CNNs) represent one of the state-of-the-art methods for image classification in a variety of fields. Because the number of training dataset images in biomedical image classification is limited, transfer learning with CNNs is frequently applied. Breast cancer is one of most common types of cancer that causes death in women. Early detection and treatment of breast cancer are vital for improving survival rates. In this paper, we propose a deep neural network framework based on the transfer learning concept for detecting and classifying breast cancer histopathology images. In the proposed framework, we extract features from images using three pre-trained CNN architectures: VGG-16, ResNet50, and Inception-v3, and concatenate their extracted features, and then feed them into a fully connected (FC) layer to classify benign and malignant tumor cells in the histopathology images of the breast cancer. In comparison to the other CNN architectures that use a single CNN and many conventional classification methods, the proposed framework outperformed all other deep learning architectures and achieved an average accuracy of 98.76%.
Movie recommender chatbot based on Dialogflow Zinke Abdurahman Baizal; Nurul Ikhsan; Ichwanul Muslim Karo Karo; Reinaldo Kenneth Darmawan; Roby Dwi Hartanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp936-947

Abstract

Currently, the online movie streaming business is growing rapidly, such as Netflix, Disney+, Amazon Prime Video, HBO, and Apple TV. The recommender system helps customers in getting information about movies that are in accordance with their wishes. Meanwhile, the development of messaging platform technology has made it easier for many people to communicate instantly. Utilizing a messaging platform to build a recommender system for movies, provides special benefits because people often access the messaging platform all the time. In the Indonesian language, there are many slang terms that the system must recognize. In this study, we build a chatbot on a messaging platform which users can interact with the system in natural language (in Indonesian language) and get recommendations. We use rule-based and maximum likelihood as a method in natural language processing (NLP), and content-based filtering for the recommendation process. The recommender system interaction is built through a conversation mechanism that will form a conversational recommender system. The interaction is based on a chatbot which is built using Dialogflow and implemented on the telegram. We use the accuracy of recommendations and user satisfaction to evaluate the system performance. The results obtained from the user study indicate that the NLP approach provides a positive experience for users. In addition, the system also produces an accuracy value of 83%.
Calculating the area of white spots on the lungs of patients with COVID-19 using the Sauvola thresholding method Retno Supriyanti; Muhammad Rifqi Kurniawan; Yogi Ramadhani; Haris Budi Widodo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp315-324

Abstract

COVID-19 is a pandemic that has occurred in the world since 2019. Researchers have carried out various ways in dealing with this disease, starting from the screening stage to the stage of treatment and therapy for COVID-19 patients. As the gateway to the COVID-19 problem, screening has an essential role in a diagnosis that leads to appropriate treatment. In this paper, we will focus on the screening stage using digital image processing techniques, namely in calculating the area of white spots in the lungs of COVID-19 patients. The white patches are an early indication of how badly COVID-19 is attacking the patient. We use X-Ray Thorax image objects as research data in this paper. Although the current experimental results show that this method has a successful performance of 71.11%, it is pretty promising for further development due to its simplicity.

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